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From:
Iván Cantador <[log in to unmask]>
Reply To:
Iván Cantador <[log in to unmask]>
Date:
Thu, 5 Jun 2014 14:57:01 +0200
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============================================================================
======
KDD 2014 - 2nd International Workshop on Multimodal Crowd Sensing
(CrowdSens'2014)
New York City, USA | August 24, 2014

Workshop site: http://sysrun.haifa.il.ibm.com/hrl/crowdsens2014/
KDD 2014 site: http://www.kdd.org/kdd2014

Workshop Twitter hashtag: #CrowdSens2014
============================================================================
======

---------------
Important dates
---------------

* Papers submission: June 14, 2014 *** extended *** 
* Notification of acceptance:  July 1, 2014
* Camera Ready:  July 8, 2014
* Workshop: August 24, 2014
* Main conference: August 24-27, 2014

-----------------
Workshop overview
-----------------

According to research conducted by the International Data Corporation (IDC),
the size of the ‘digital universe’ in 2010 
(i.e., the amount of information which is stored digitally) surpassed one
Zettabyte (ZB) for the first time in history 
and it now stands at about 1.8 ZB. This massive expansion in the size of the
amount of information appears to be exceeding 
Moore’s Law. It is also estimated that about 70% of this information is
generated by individuals. The ubiquitous availability 
of computing technology, in particular smartphones, tablets, laptops and
other easily portable devices, and the adoption of 
social networking sites, make it possible to be connected and continuously
contribute to this massively distributed information
publishing process. 

By doing so, users are (unconsciously) acting as social sensors, whose
sensor readings are their manually generated data. 
People document their daily life experiences, report on their physical
locations and social interactions with others, express
opinions and provide diverse observations on both the physical world
(sights, sounds, smells, feelings, etc.) and the online
world (news, music, events, etc.). Such massive amounts of ubiquitous social
sensors, if wisely utilized, can provide new forms
of valuable information that are currently not available by any traditional
data collection methods including real physical sensors,
and can be used to enhance decision making processes.  

It has been shown over and over that reports on real world events, such as
the Japan’s Earthquake and Tsunami, the Arab Spring
uprisings, and the England’s riots happened in 2011, are much faster
propagated within the network of social sensors (e.g. on Twitter)
than they are processed by traditional means (e.g. seismic sensor reading
analysis, police emergency reports, news media coverage).
In these cases, human observers can be exploited to interpret and enrich
such integrated sensor-derived information. As an example,
both journalists and opinion makers now make increasing usage of massive
data collected from social sensors in order to study public
opinions, and discover new perspectives of daily stories.  As another
example, within a smart city scenario, social sensors can contribute
important information about the daily city life through various channels,
such as social media, SMS, and reports to the city operation center.
Such social sensors can enrich the existing information currently collected
by the city physical sensors (e.g. traffic and camera sensors),
helping to reduce uncertainty, and leading to a better envision and
comprehension of the magnitude of potential problems and situations.

Effective mining, analyzing, fusing, and exploiting information sourced from
multimodal physical and social sensor data sources is still
an open and exciting challenge. Many factors here add to the complexity of
the problem, including the real-time element of the data processing;
the heterogeneity of the sources, from physical sensors data to posts on
social media; and the ubiquitous and noisy nature of the human-sensor
generated information, which can be written in an informal style,
duplicated, incomplete or even incorrect.

The 2nd International Workshop on Multimodal Crowd Sensing (CrowdSens 2014)
will provide an open forum for researchers from various domains 
such as data management, data mining, information retrieval, and semantic
web, for discussing the above challenges.

------------------
Workshop objective
------------------

The main goal of CrowdSens 2014 is to become a major international forum for
researchers and practitioners from different research areas such as
Social Web, Semantic Web, Natural Language Processing, Information
Extraction, Data Mining, Information Retrieval, User Modelling,
Personalization,
Stream Processing, and Sensor Networks, who focus their work on
user-generated contents.
Our aim is to stimulate discussions about how the knowledge embedded in
human sensor data can be collected, extracted, modeled, analyzed, 
integrated, summarized, and finally exploited. Ideas for innovation will
extend through different fields, from data mining, user modelling,
personalization, recommendation, information retrieval, and business
intelligence, to name a few. Different research lines, backgrounds, 
perspectives, and degrees of expertise will be present at the workshop, and
thus very interesting multidisciplinary discussions, collaborations,
and work synergies between the workshop attendees are expected as one of the
main outcomes of the event.

Inspired by this year's KDD conference focus on "Data Mining for Social
Good", the 2nd International Workshop on Multimodal Crowd Sensing
(CrowdSens'2014)
aims to gather researchers and practitioners around the topic of "Harnessing
crowd sensors for social good." The main goal of the workshop is therefore
to explore how analyzing, fusing and exploiting information from multimodal
physical and social sensor (people) data sources can help tackle societal
challenges including citizen empowerment, environment protection, direct
democracy, education, aging and well being, smart city living environment,
disaster management, etc. 

------------------
Topics of interest
------------------

Themes and topics of interest at this workshop include, but are not limited
to:

* Data acquisition methods for crowd sensing
   * Physical world crowd data capture
   * Multimedia crowd data capture (e.g. SMS, MMS, CDRs, transcripts)
   * Real-time data acquisition methods
   * Massive scale social sensor monitoring and crawling
   * Predictive models for social data acquisition
   * Scheduling, prioritization and sampling methods

* Data models for crowd sensing
   * Social sensor event models
   * Social sensor data representation
   * Social sensor context representation 
   * Spatio-temporal models for crowd sensing
   * Multimodal data models for crowd sensing
   * Semantic models for crowd sensing
   * Uncertainty models for incomplete and noisy social sensors data
   * Trust and authorization models for crowd sensing
   * Privacy in crowd sensing

* Novel data processing, analysis, and classification methods
   * Data cleansing for crowd sensing (e.g. real-time duplicates detection)
   * Feature extraction, Entity analytics and novel NLP methods
   * Context extraction and prediction using multimodal sources
   * Uncertainty estimation and predictive analytics
   * Data mining methods under incomplete and noisy data (e.g. online
clustering, categorization, classification)
   * Opinion mining, sentiment analysis methods for crowd sensing
   * Trends, bursts, anomalies and outliers detection over large scale
social sensor data
   * Network analysis, information propagation and influence detection
methods for crowd sensing
   * Crowd behavioural analysis and prediction
   * Real-time community detection and analysis
   * Social stream processing methods (e.g. top-k querying, filtering,
sampling)

* Event detection, fusion, and summarization methods
   * Event detection methods (under uncertainty, incomplete or noisy
settings)
   * Event story detection 
   * Detection of developing events 
   * Event uncertainty estimation 
   * Event time and location estimation
   * Methods for event data delivery
   * Methods for event data reporting, summarization or visualization
   * Pattern recognition methods
   * Multimodal data fusion methods 

* Evaluation methods for crowd-sensing
   * Quality metrics and key performance indicators for crowd sensing
   * Benchmarks and evaluation methodologies for crowd sensing
 
* Applications of crowd sensing
   * News mining from social sensors (e.g. emerging story detection)
   * Infotainment (e.g. event discovery and recommendation)
   * Disaster management (e.g. weather monitoring, disaster prediction)
   * Public safety (e.g. prediction of developing situation and sentiments)
   * Public health (e.g. epidemic monitoring, infectious disease outbreak
detection)
   * Transportation (e.g. prediction of traffic loads, detection of hazards)

   * Finance (e.g. market monitoring)
   * Cyber security (e.g. Counter terrorism, dark web monitoring)
   * Government and Politics (e.g. Voice of Citizen, opinion mining)
   * Retail and consumer products (e.g. Voice of Customer, demand sensing)

---------------------
Submission guidelines
---------------------

We invite two main types of contributions: full papers (6-8 papers) pages)
and short papers (2-4 pages). Both types of contributions could be new
research ideas, position statements, critiques of existing approaches, or
experiment reports.

Submitted papers will be evaluated according to their originality, technical
content, style, clarity, and relevance to the workshop. Each paper will be
reviewed by at least three independent referees.

Manuscripts should be submitted electronically, in PDF format and formatted
using the ACM camera-ready templates available at:
http://www.acm.org/sigs/publications/proceedings-templates

All submissions will be done electronically via the CrowdSens 2014 Web
submission system:
http://www.easychair.org/conferences/?conf=crowdsens2014

Accepted papers will be published in online proceedings. 

At least one author of each accepted paper must register for the conference.
Information about registration will be provided at KDD 2014 Web page:
http://www.kdd.org/kdd2014 

------------
Organization
------------

* Haggai Roitman, IBM Research - Haifa, Israel
* Miriam Fernandez, Knowledge Media Institute, UK
* Ivan Cantador, Universidad Autonoma de Madrid, Spain


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